basics.sagews

R Basics

Just a demo, how R can be called from Sage to do something useful.

print r

R Interpreter

x = r.c((1,2,3,-1, 2,2.2))
print x
type(x)
print x.summary()

[1] 1.0 2.0 3.0 -1.0 2.0 2.2 <class 'sage.interfaces.r.RElement'> Min. 1st Qu. Median Mean 3rd Qu. Max. -1.000 1.250 2.000 1.533 2.150 3.000

%r
x <- c(1,4,3,4,3,2,3,2,3,6,3)
summary(x)

Min. 1st Qu. Median Mean 3rd Qu. Max. 1.000 2.500 3.000 3.091 3.500 6.000

%r
x[1]
x[2]

[1] 1 [1] 4

%r
z <- sample(-5:5, 30, replace=T)
z
summary(z)

[1] 5 5 -2 5 -5 -4 -3 -4 1 -1 -1 -3 0 0 -2 1 -4 -2 -2 -2 -5 -5 -4 1 1 4 -2 -1 0 -4 Min. 1st Qu. Median Mean 3rd Qu. Max. -5.00 -3.75 -2.00 -1.10 0.75 5.00

z2 = r.sample(range(-5,5), 10, replace=False)
z2

[1] 0 4 -1 -5 2 -2 -3 -4 3 1

%r
plot(-5:5, sample(-5:5, 11, replace=F))

Linear Regression

%r
X <- sort(runif(100, -5,5))
Y <- -2 + 1.1 * X + rnorm(100, 0, 5)
lmobj <- lm(Y ~ X)
summary(lmobj)

Call: lm(formula = Y ~ X) Residuals: Min 1Q Median 3Q Max -16.5313 -3.3934 -0.4112 3.0911 14.8677 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.2975 0.5098 -4.507 1.82e-05 *** X 1.3803 0.1764 7.824 6.02e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.094 on 98 degrees of freedom Multiple R-squared: 0.3845, Adjusted R-squared: 0.3782 F-statistic: 61.21 on 1 and 98 DF, p-value: 6.019e-12